User-interruption in human-computer interaction (HCI) is an increasingly important problem. Many of the useful advances in intelligent and multitasking computer systems have the significant side effect of greatly increasing user-interruption. This previously innocuous HCI problem has become critical to the successful function of many kinds of modern computer systems. Unfortunately, no HCI design guidelines exist for solving this problem. In fact, theoretical tools do not yet exist for investigating the HCI problem of user-interruption in a comprehensive and generalizable way. This report asserts that a single unifying definition of user-interruption and the accompanying practical taxonomy would be useful theoretical tools for driving effective investigation of this crucial HCI problem. These theoretical tools are constructed here. A comprehensive analysis is conducted through the existing literature. Theoretical constructs from several relevant but diverse fields are identified and discussed. A unifying definition of user-interruption is synthesized. This new definition is supported with an array of postulates, assertions, and a taxonomy of human interruption to facilitate its practical application.

Peripheral information is information that is not central to a person's current task, but provides the person the opportunity to learn more, to do a better job, or to keep track of less important tasks. Though peripheral information displays are ubiquitous, they have been rarely studied. For computer users, a common peripheral display is a scrolling text display that provides announcements, sports scores, stock prices, or other news. In this paper, we investigate how to design peripheral displays so that they provide the most information while having the least impact on the user's performance on the main task. We report a series of experiments on scrolling displays aimed at examining tradeoffs between distraction of scrolling motion and memorability of information displayed. Overall, we found that continuously scrolling displays are more distracting than displays that start and stop, but information in both is remembered equally well. These results are summarized in a set of design recommendations.

At first glance it seems absurd that busy people doing important jobs should want their computers to interrupt them. Interruptions are disruptive and people need to concentrate to make good decisions. However, successful job performance also frequently depends on people's abilities to (a) constantly monitor their dynamically changing information environments, (b) collaborate and communicate with other people in the system, and (c) supervise background autonomous services. These critical abilities can require people to simultaneously query a large set of information sources, continuously monitor for important events, and respond to and communicate with other human operators. Automated monitoring

Attentive systems pay attention to what users do so that they can attend to what users need. Such systems track user behavior, model user interests, and anticipate user desires and actions. Because the general class of attentive systems is broad- ranging from human butlers to web sites that profile users- we have focused specifically on attentive information systems, which observe user actions with information resources, model user information states, and suggest information that might be helpful to users. In particular, we describe an implemented system, Simple User Interest Tracker (Suitor), that tracks computer users through multiple channels- gaze, web browsing, application focus- to determine their interests and to satisfy their information needs. By observing behavior and modeling users, Suitor finds and displays potentially relevant information that is both timely and non-disruptive to the users ’ ongoing activities.

Abstract not found

A fundamental aspect of multiple task management is attending to new stimuli and integrating associated task requirements into an ongoing task set- this is ``interruption management'' (IM). Anecdotal evidence and field studies indicate the frequency and consequences of interruptions, however experimental investigations of mechanisms influencing IM are scarce. Interruptions on commercial flightdecks are numerous, of various forms, and have been cited as contributing factors in many aviation incident and accident reports. This research grounds an experimental investigation of flightdeck interruptions in a proposed IM stage model. This model organizes basic research, identifies influencing mechanisms, and suggests appropriate dependent measures for IM. Fourteen airline pilots participated in a flightdeck simulation experiment to investigate the general effects of performing an interrupting task and interrupted procedure, and the effects of specific task factors: (1) modality; (2) embeddedness, or goal­level, of an interruption; (3) strength of association, or coupling­strength, between interrupted tasks; (4) semantic similarity; and (5) environmental stress. General effects of interruptions were extremely robust. All individual task factors significantly affected interruption management, except ``similarity.'' Results extend the Interruption Management model, and are interpreted for their implications for interrupted flightdeck performance and intervention strategies for mitigating their effects on the flightdeck.

In the first part of this report, four stages of information processing,-- attentional filtering, integration and inference, choice, and response execution,-- are outlined, each of which can be automated. Such automation can vary in its reliability. We distinguish between automation that is perfectly reliable, automation that fails “catastrophically ” and automation whose reliability is high, but understandably imperfect (e.g., drawing inference from inherently ambiguous data or noisy sensors). In the case of imperfect automation, we also distinguish between cases when the operator is and is not aware of the imperfection. We then describe the various human performance costs resulting from these different states and levels of unreliability, as they are relevant to the different stages of automation. We emphasize empirical data from automated attention filtering (Stage 1) in such systems as target cueing alarms, or intelligent information management. Many of these costs relate to the distribution of attention in the environment. In the second part of the report, we then describe a model of the influences on how pilots distribute and allocate visual attention in dynamic environments, in order to maintain situation awareness. The model incorporates bottom up influences on attention allocation related

In this paper, we compare the advanced automation characteristics of Model-based Predictive Controllers (MPC) employed in process control to the more thoroughly studied Flight Management System (FMS) employed in airline cockpits. Our analysis shows that operator interaction with cockpit automation and MPC automation may differ in terms of the level of the control loops at which human control actions are directed. Cockpit automation appears to afford human control actions at outer loops through the specification of a flight path or selection of control modes and flight-path parameters. Inner loops, such as the control of flight surfaces, are controlled by the automation. In contrast, MPC affords operator control at the control variable level because operators are required to manipulate limits on those variables. Outer loops are managed by the automation. These differences in level may explain why we observed refinery operators asking questions of the automation pertaining to control intervention whereas reported studies of pilots ’ interaction with cockpit automation do not. This could also explain why we did not observe refinery operators having difficulty with automated mode changes, whereas reports from FMS interaction show that mode discrimination is a difficult and challenging task. Keywords: Cognitive task analysis; Process control; Predictive control; Optimization.

Automation does not mean humans are replaced; quite the opposite. Increasingly, humans are asked to interact with automation in complex and typically large-scale systems, including aircraft and air traffic control, nuclear power, manufacturing plants, military systems, homes, and hospitals. This is not an easy or error-free task for either the system designer or the human operator/automation supervisor, especially as computer technology becomes ever more sophisticated. This review outlines recent research and challenges in the area, including taxonomies and qualitative models of human-automation interaction; descriptions of automation-related accidents and studies of adaptive automation; and social, political, and ethical issues.

The road to technology-centered systems is paved with user-centered intentions.